arXiv subjects

Joohan Park

Publications and source records attributed to Joohan Park.

3 recordsLinked to original sources

arXiv2023

On the Spatial-Wideband Effects in Millimeter-Wave Cell-Free Massive MIMO

In this paper, we investigate the spatial-wideband effects in cell-free massive MIMO (CF-mMIMO) systems in mmWave bands. The utilization of mmWave frequencies brings challenges such as signal attenuation and the need for denser networks like ultra-dense networks (UDN) to maintain communication performance. CF-mMIMO is introduced as a solution, where distributed access points (APs) transmit signals to a central processing unit (CPU) for joint processing. CF-mMIMO offers advantages in reducing non-line-of-sight (NLOS) conditions and overcoming signal blockage. We investigate the synchronization problem in CF-mMIMO due to time delays between APs. It proposes a minimum cyclic prefix length to mitigate inter-symbol interference (ISI) in OFDM systems. Furthermore, the spatial correlations of channel responses are analyzed in the frequency-phase domain. The impact of these correlations on system performance is examined. The findings contribute to improving the performance of CF-mMIMO systems and enhancing the effective utilization of mmWave communication.

cs.IT↗

arXiv2022

Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID Data

In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem. To address this problem, we propose a novel diffusion strategy for machine learning (ML) models (FedDif) to maximize the performance of the global model with non-IID data. FedDif enables the local model to learn different distributions before parameter aggregation by passing the local models through users via device-to-device communication. Furthermore, we theoretically demonstrate that FedDif can circumvent the weight-divergence problem. Based on this theory, we propose a communication-efficient diffusion strategy for ML models that can determine the trade-off between learning performance and communication cost using auction theory. The experimental results show that FedDif improves the top-1 test accuracy by up to 34.89\% and reduces communication costs by 14.6% to a maximum of 63.49%.

cs.DC↗

arXiv2020

Autonomous Power Allocation based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network

For Device-to-device (D2D) communication of Internet-of-Things (IoT) enabled 5G system, there is a limit to allocating resources considering a complicated interference between different links in a centralized manner. If D2D link is controlled by an enhanced node base station (eNB), and thus, remains a burden on the eNB and it causes delayed latency. This paper proposes a fully autonomous power allocation method for IoT-D2D communication underlaying cellular networks using deep learning. In the proposed scheme, an IoT-D2D transmitter decides the transmit power independently from an eNB and other IoT-D2D devices. In addition, the power set can be nearly optimized by deep learning with distributed manner to achieve higher cell throughput. We present a distributed deep learning architecture in which the devices are trained as a group but operate independently. The deep learning can attain near optimal cell throughput while suppressing interference to eNB.

eess.SP↗